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Track and Visualize Experiments (expert)

Audience: Users who want to make their own progress bars or integrate new experiment managers.


Change the progress bar

If you’d like to change the way the progress bar displays information you can use some of our built-in progress bard or build your own.


Use the TQDMProgressBar

To use the TQDMProgressBar pass it into the callbacks Trainer argument.

from pytorch_lightning.callbacks import TQDMProgressBar

trainer = Trainer(callbacks=[TQDMProgressBar()])

Use the RichProgressBar

The RichProgressBar can add custom colors and beautiful formatting for your progress bars. First, install the `rich <https://github.com/Textualize/rich>`_ library

pip install rich

Then pass the callback into the callbacks Trainer argument:

from pytorch_lightning.callbacks import RichProgressBar

trainer = Trainer(callbacks=[RichProgressBar()])

The rich progress bar can also have custom themes

from pytorch_lightning.callbacks import RichProgressBar
from pytorch_lightning.callbacks.progress.rich_progress import RichProgressBarTheme

# create your own theme!
theme = RichProgressBarTheme(description="green_yellow", progress_bar="green1")

# init as normal
progress_bar = RichProgressBar(theme=theme)
trainer = Trainer(callbacks=progress_bar)

Customize a progress bar

To customize either the TQDMProgressBar or the RichProgressBar, subclass it and override any of its methods.

from pytorch_lightning.callbacks import TQDMProgressBar


class LitProgressBar(TQDMProgressBar):
    def init_validation_tqdm(self):
        bar = super().init_validation_tqdm()
        bar.set_description("running validation...")
        return bar

Build your own progress bar

To build your own progress bar, subclass ProgressBarBase

from pytorch_lightning.callbacks import ProgressBarBase


class LitProgressBar(ProgressBarBase):
    def __init__(self):
        super().__init__()  # don't forget this :)
        self.enable = True

    def disable(self):
        self.enable = False

    def on_train_batch_end(self, trainer, pl_module, outputs, batch_idx):
        super().on_train_batch_end(trainer, pl_module, outputs, batch_idx)  # don't forget this :)
        percent = (self.train_batch_idx / self.total_train_batches) * 100
        sys.stdout.flush()
        sys.stdout.write(f"{percent:.01f} percent complete \r")


bar = LitProgressBar()
trainer = Trainer(callbacks=[bar])

Integrate an experiment manager

To create an integration between a custom logger and Lightning, subclass LightningLoggerBase

from pytorch_lightning.loggers import Logger


class LitLogger(Logger):
    @property
    def name(self) -> str:
        return "my-experiment"

    @property
    def version(self):
        return "version_0"

    def log_metrics(self, metrics, step=None):
        print("my logged metrics", metrics)

    def log_hyperparams(self, params, *args, **kwargs):
        print("my logged hyperparameters", params)

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